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A Joint Communication and Computation Framework for Digital Twin over Wireless Networks

Zhaohui Yang, Mingzhe Chen, Yuchen Liu, Zhaoyang Zhang

TL;DR

This work tackles the problem of low-latency DT operation over wireless networks by formulating a joint communication and computation design that minimizes the total transmission delay under energy budgets and DT accuracy constraints. It introduces an alternating optimization framework that iteratively solves device scheduling, power control, and data offloading, with a dual-method solution providing closed-form scheduling in the multi-device setting and a specialized online two-phase scheme for the single-device case. The key contributions include (i) a convexified, tractable approach to joint CP under data causality and FDMA constraints, (ii) a detailed complexity analysis and convergence guarantee, and (iii) numerical results showing up to 51.2% delay reduction compared to baseline schemes. The approach offers practical benefits for DT deployments in wireless networks by balancing energy expenditure, data quality, and latency, enabling more responsive digital-physical interoperation in metaverse-like scenarios.

Abstract

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. For the special case with one physical device, the optimal number of transmission times is reveled. Based on the theoretical findings, the original problem is transformed into a simplified problem and the optimal device scheduling can be found. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2\% compared to the conventional schemes.

A Joint Communication and Computation Framework for Digital Twin over Wireless Networks

TL;DR

This work tackles the problem of low-latency DT operation over wireless networks by formulating a joint communication and computation design that minimizes the total transmission delay under energy budgets and DT accuracy constraints. It introduces an alternating optimization framework that iteratively solves device scheduling, power control, and data offloading, with a dual-method solution providing closed-form scheduling in the multi-device setting and a specialized online two-phase scheme for the single-device case. The key contributions include (i) a convexified, tractable approach to joint CP under data causality and FDMA constraints, (ii) a detailed complexity analysis and convergence guarantee, and (iii) numerical results showing up to 51.2% delay reduction compared to baseline schemes. The approach offers practical benefits for DT deployments in wireless networks by balancing energy expenditure, data quality, and latency, enabling more responsive digital-physical interoperation in metaverse-like scenarios.

Abstract

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. For the special case with one physical device, the optimal number of transmission times is reveled. Based on the theoretical findings, the original problem is transformed into a simplified problem and the optimal device scheduling can be found. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2\% compared to the conventional schemes.
Paper Structure (15 sections, 2 theorems, 38 equations, 9 figures, 1 algorithm)

This paper contains 15 sections, 2 theorems, 38 equations, 9 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Algorithm Design
  6. Device Scheduling Subproblem
  7. Power Control Subproblem
  8. Data Offloading Subproblem
  9. Algorithm Analysis
  10. Optimization with One Physical Device
  11. Initial Phase
  12. Recursion Phase
  13. Overall Phase
  14. Simulation Results
  15. Conclusions

Key Result

Lemma 1

The optimal solution of problem alg2min1_2_2 is and where $\lambda_{1nk}\geq 0$, $\lambda_{2n}\geq 0$, and $0\leq \lambda_{3nk}\leq 1$ are the corresponding dual variables associated with constraints (alg2min1_2_2a)-(alg2min1_2_2c).

Figures (9)

  • Figure 1: The application scenario of DT based communication system.
  • Figure 2: The considered system model of DNTs.
  • Figure 3: The transmission delay versus the maximum transmit power of each physical device with $K=14$.
  • Figure 4: The transmission delay versus the maximum transmit power of each physical device with $K=10$.
  • Figure 5: The transmission delay versus the number of physical devices with maximum transmit power 2 dBm.
  • ...and 4 more figures

Theorems & Definitions (3)

  • Lemma 1
  • proof
  • Lemma 2